Journal of Chromatography A, 1343 (2014) 18–25
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Determination of fungicides in white grape bagasse by pressurized liquid extraction and gas chromatography tandem mass spectrometry夽 Maria Celeiro a , Maria Llompart a,∗ , J. Pablo Lamas a , Marta Lores a , Carmen Garcia-Jares a , Thierry Dagnac b a Department of Analytical Chemistry, Nutrition and Food Science, Faculty of Chemistry, Campus Vida, University of Santiago de Compostela, E-15782 Santiago de Compostela, Spain b Galician Institute for Food Quality (INGACAL), Agronomic and Agrarian Research Centre (CIAM), E-15080 A Coru˜ na, Spain
a r t i c l e
i n f o
Article history: Received 27 December 2013 Received in revised form 20 March 2014 Accepted 21 March 2014 Available online 28 March 2014 Keywords: Fungicides Bagasse Ultrasound-assisted extraction Pressurized liquid extraction GC–MS GC–TQ-MS
a b s t r a c t Ultrasound-assisted extraction (UAE) and pressurized liquid extraction (PLE) followed by gas chromatography–triple quadrupole-mass spectrometry (GC TQ-MS) were used for the rapid determination of 11 fungicides (metalaxyl, cyprodinil, procymidone, iprovalicarb, myclobutanyl, kresoxim-methyl, benalaxyl, fenhexamide, tebuconazole, iprodione and dimethomorph) in white grape bagasse. The extractions were optimized on real non-spiked samples by means of experimental design and the optimal conditions were selected to achieve the method validation. The PLE procedure showed much higher efficiency than UAE for the target fungicides. Under the selected extraction conditions, PLE showed satisfactory linearity, repeatability and reproducibility. Recoveries for the majority of studied fungicides were higher than 80% with relative standard deviations (RSD) lower than 12%. Limits of detection (LODs) for GC TQ-MS were very low, at the sub ng g−1 for the majority of the target fungicides, well below the European maximum residue limits (MRLs) for wine and table grapes, and vine leaves. Eighteen white grape bagasse samples were analyzed and nine out of eleven targets were detected in the samples. Seven of them were detected in more than 50% of the samples and most samples contained at least four of the target analytes. The most frequently found compounds were tebuconazole and dimethomorph with concentrations between 1.6–130 and 2.0–1788 ng g−1 , respectively. Some samples showed high levels of many of the studied fungicides (high ng g−1 , even g g−1 for cyprodinil, fenhexamide, iprodione and dimethomorph), but all of them below the European maximum residue limits (MRLs) for wine grapes. © 2014 Elsevier B.V. All rights reserved.
1. Introduction There is an increasing interest regarding health and safety aspects associated with the use of pesticides and the presence of their residues in food and drinks. Pesticides are used to control pests in vegetables, fruits or cereal grains among others [1]. Fungicides are a class of pesticides widespread used in viticulture to avoid fungi infection of Vitis plants, being mainly used for treating grey rot (Botrytis cinerea), mildew (Plasmopara viticola) and oidium (Uncinula necator). Their use has brought many
夽 Presented at the XIII Scientific Meeting of the Spanish Society of Chromatography and Related Techniques (SECyTA2013), 8–11 October 2013, Puerto de la Cruz, Tenerife, Canary Islands, Spain. ∗ Corresponding author. Tel.: +34 881814225; fax: +34 981595012. E-mail address: [email protected] (M. Llompart). http://dx.doi.org/10.1016/j.chroma.2014.03.057 0021-9673/© 2014 Elsevier B.V. All rights reserved.
benefits with respect to enhanced quality of produced crops, but there are concerns about the presence of their residues in crops, which may pose a health hazard to the consumers. In addition, several studies have shown that some fungicides and their degradation products can be found in musts and some of them are frequently found at low concentration levels in the final commercial wine [2–5]. Bagasse (also called marc) is the residue left behind after the juice has been removed from bunch of grapes during winemaking. There is an increasing interest, supported by environmental and economic reasons, to recover and exploit these wastes from the food industry, because such residues can be used as a source of natural bioactive compounds, which could in turn be used in pharmaceutical, cosmetics or back in the food industry. Therefore, the levels of fungicides in grape bagasse must be controlled in order to avoid environmental pollution and human exposure to these compounds [6].
M. Celeiro et al. / J. Chromatogr. A 1343 (2014) 18–25 Table 1 European maximum residue limits (MRLs) for wine and table grapes and vine leaves [7]. Fungicide Metalaxyl Cyprodinil Procymidone Iprovalicarb Myclobutanyl Kresoxim-methyl Benalaxyl Fenhexamide Tebuconazole Iprodione Dimethomorph
Table grapes (mg kg−1 )
Wine grapes (mg kg−1 ) 1 5 0.01 2 1 1 0.3 5 2 10 3
Vine leaves (mg kg−1 )
2 5 0.01 2 1 1 0.3 5 2 10 3
0.05 0.05 0.01 0.05 0.02 0.05 0.05 0.05 0.05 0.02 0.01
The European Community establishes the maximum residue limits (MRLs) for different fungicides in wine and table grapes as well as in vine leaves through EC Regulation 396/2005 and their subsequent amendments [7], but not any harmonized MRLs have been laid down in the European Union for pesticides in bagasse or wine. European MRLs set for the target fungicides in wine and table grapes, and in vine leaves are shown in Table 1. Nevertheless, a look at the scientific literature evidences the lack of studies devoted to the development of methodology for the determination of fungicides in bagasse samples whereas in grapes, other fruits and vegetables, environmental friendly procedures including microwave assisted extraction (MAE) [8], QuEChERS [9–12], solid phase micro-extraction (SPME) [13,14] or matrix–solid phase dispersion (MSPD) [15–17] are substituting traditional methodologies like Soxhlet extraction [18,19]. Pressurized liquid extraction (PLE) was also employed to determine various chemical classes of fungicides in different matrices such as mushroom compost [20], vineyard and agricultural soils [21,22], green leafy vegetables [23] or green tea [24]. The analytical methods for detecting and quantifying fungicides in different fruits are generally based on liquid chromatography (LC) or gas chromatography (GC). The decision to use either LC or GC is based on the physico-chemical properties of the target analytes. The on-line coupling of efficient liquid chromatography or gas chromatography separation with mass spectrometry detection (LC–MS or GC–MS) has became an accepted technique for performing regulatory monitoring. GC–MS is an advantageous and powerful technique for the determination of (semi)volatile and low polarity fungicides in vegetable samples and LC–MS is the best choice for substances with low volatility and/or thermal instability [25]. Liquid or gas chromatography in combination with tandem mass spectrometry (MS–MS) is a valuable approach that improves selectivity and analyte sensitivity, minimizing most of the matrix interferences. In this way, triple quadrupole (TQ) working under
19
MS/MS mode can achieve lower limits of detection than simple quadrupole GC–SQ–MS or LC–SQ–MS and increase the selectivity and specificity of the method. LC TQ-MS was successfully employed to determine fungicides in cereals, vegetables and fruits [9], grapes [10] or food (lettuce, tomato, apple and grapes) [11] and although there are few references of GC TQ-MS to determine fungicides in vegetables or fruit samples, this technique was recently used to analyze more than 140 pesticides in vegetables [26,27]. The aim of this study was to develop and validate a method to analyze 11 fungicides from different chemical classes (metalaxyl, cyprodinil, procymidone, iprovalicarb, myclobutanyl, kresoximmethyl, benalaxyl, fenhexamide, tebuconazole, iprodione and dimethomorph) in white grape bagasse, based on pressurized liquid extraction-gas chromatography–triple quadrupole-mass spectrometry. This study also aimed at comparing GC–TQ–MS with GC–SQ–MS to determine whether the use of the former technique is an improvement for the detection of the target fungicides. Finally, the validated method was used to identify and quantify the studied fungicides in real white grape bagasse. To the best of our knowledge, this is the first time that PLE–GC TQ-MS is applied to the analysis of fungicides in bagasse samples. 2. Materials and methods 2.1. Chemicals, materials and samples The studied compounds, their chemical names, CAS numbers, suppliers and purity are summarized in Table 2. Acetone, ethyl acetate and n-hexane were provided by SigmaAldrich Chemie GmbH (Steinheim, Germany). Methanol was provided by Merk (Darmstadt, Germany). Sand (200–300 m) was purchased from Scharlau (Barcelona, Spain) and sodium chloride (NaCl) was provided by Prolabo (Leuven, Belgium). Individual stock solutions of each compound were prepared in methanol or ethyl acetate. Further dilutions and mixtures were prepared in acetone (sample fortification solutions) and hexane/acetone (calibration standards). All solutions were stored in amber glass vials at −20 ◦ C. All solvents and reagents were of analytical grade. Bagasse samples were selected among five different varieties of ˜ Caino, ˜ Loureira, Treixadura and white grape of Galicia (Albarino, Godello). Samples were dried at 60 ◦ C for 24 h before use, and subsequently were crushed in a conventional coffee grinder and pulverized in a porcelain mortar using a porcelain pestle until a homogeneous mixture was obtained (ca. 5 min). 2.2. UAE procedure Ultrasound-assisted extraction (UAE) was carried out using an ultrasonic cleaning bath with a working frequency of 50 kHz and
Table 2 Target compounds: purity, suppliers, CAS numbers, and GC–MS parameters. Fungicides
Purity (%)a
CAS
Retention time (min)
Quantification and identification ionsb
Metalaxyl Cyprodinil Procymidone Iprovalicarb Myclobutanyl Kresoxim-methyl Benalaxyl Fenhexamide Tebuconazole Iprodione Dimethomorph
99.5 97.5 99.5 99 97.5 98 98 99 99 97.5 98
70630-17-0 32809-16-8 88671-89-0 140923-17-7 143390-89-0 71626-11-4 121552-61-2 126833-17-8 107534-96-3 36734-19-7 110488-70-5
9.67 10.77 11.07 11.74/11.90 11.95 11.89 12.94 13.28 13.47 13.80 18.87/19.43
192.1 (42), 160.1 (57), 206.1 (100) 210.1 (10), 224.0 (100), 225.1 (61) 96.0 (203), 283 (100), 285 (65) 116.1 (99), 119.0 (71), 134.0 (100) 150.0 (51), 179.0 (100), 245.0 (45) 116 (214), 131 (100), 206.1 (92) 91.0 (45), 148.1 (100), 206.1 (26) 55.0 (40), 97.0 (100), 177.0 (34) 83.0 (84), 125 (145), 250.0 (100) 186.9 (51), 244.8 (20), 313.9 (100) 165.0 (32), 301.0 (100), 387.1 (29)
a b
Dr. Ehrenstorfer (Ausburg, Germany). Numbers in brackets are the relative ion abundances, %.
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M. Celeiro et al. / J. Chromatogr. A 1343 (2014) 18–25
Table 3 GC TQ-MS retention time and selected transitions. Fungicides
Retention time (min)
Precursor iona
Product iona
Collision energya
Metalaxyl Cyprodinil Procymidone Iprovalicarb Myclobutanyl Kresoxim-methyl Benalaxyl Fenhexamide Tebuconazole Iprodione Dimethomorph
11.62 12.91 13.27 14.04 14.26 14.20 15.36 15.78 16.02 16.58 23.92/24.60
206.1,234.1 224.1,225.1 283.0,283.0 116.0,116.0 179.1,179.1 206.1,206.1 234.1,266.1 177.0,301.1 250.1,252.1 314.0, 316.0 301.1,387.1
132.0,174.1 208.1,210.1 96.0,255.0 55.1,98.0 125.0,152.1 131.1,116.1 174.1,148.1 113.1,97.0 125.1,127.1 245.0, 247.0 165.1,301.1
10,10 20,18 15,10 10,15 15,15 15,15 10,10 10,15 20,20 15, 15 10,12
a
Underlined values are the quantification transitions.
110 W of power (Ultrasound Med-II, J.P. Selecta, Barcelona, Spain). 0.5 g of bagasse were mixed with 5 mL of the appropriate extraction solvent (ethyl acetate, hexane:acetone (1:1 v/v), methanol or hexane) in a 10 mL vial that was placed in the ultrasound bath. The mixture bagasse sample-solvent was sonicated (15 min) at different temperatures (25 or 45 ◦ C) and with NaCl (0 or 20% w/v). Afterwards, the supernatants were filtered through 0.45-m PTFE microporous filters (25 mm diameter), evaporated under a gentle nitrogen stream and reconstituted in ethyl acetate. The extracts, diluted when necessary, were analyzed by GC–MS and GC TQ-MS. 2.3. PLE procedure Extractions were performed on an ASE 150 (Dionex, Co., Sunyvale, CA, USA), equipped with 10 mL stainless steel cells and 60 mL collection vials. Two cellulose filters (Dionex) were placed at each end of the PLE cell. 0.5 g of sample were weighted and introduced into the cell, where previously 1 g of clean sand (200–300 m mesh particle size, Scharlau, Barcelona, Spain) was placed. For the preparation of fortified samples, the sample bagasse was spiked with 10 L of the corresponding acetone solution of the target compounds to get the desired final concentration. Finally, the dead volume of the cell was filled with sand. The cell was tightly closed and placed into PLE system. Extractions were performed by preheating the cell before filling with solvent (preheat method). The extraction pressure was 1500 psi, the flush volume was 60%, and the purge time 60 s. Hexane:acetone (1:1, v/v) was employed as extraction mixture. Three temperatures were studied (80, 100 and 120 ◦ C) at 5, 10 and 15 min. In all cases, the extracts were leveled to a final volume of 20 mL and were analyzed by GC–MS and GC TQ-MS. 2.4. GC–MS analysis The GC–MS analysis was performed using an Agilent 7890A (GC)-Agilent 5975C inert MSD with triple axis detector and an
Agilent 7693 autosampler from Agilent Technologies (Palo Alto, CA, USA). The temperatures of the transfer line, the quadrupole and the ion source were set at 290, 150 and 230 ◦ C, respectively. The system was operated by Agilent MSD ChemStation E.02.00.493 software. Separation was carried out on a cross-linked 5%phenyl polysilphenylen-siloxane TR-5 MS capillary column (30 m × 0.25 mm i.d., 0.25 m film thickness) obtained from Thermo Scientific (Palo Alto, CA, USA). Helium (purity 99.999%) was employed as carrier gas at a constant column flow of 1.0 mL min−1 . The GC oven temperature was programmed from 100 ◦ C (held 2 min) to 200 ◦ C at 20 ◦ C min−1 , to 260 ◦ C at 10 ◦ C min−1 and 20 ◦ C min−1 to 290 ◦ C. Pulsed splitless mode was used for injection (30 psi, held 1.2 min). After 1 min, the split valve was opened and the injector temperature was kept at 260 ◦ C. The injection volume was 1 L. The mass spectra detector (MSD) operated in selected ion monitoring (SIM) mode, monitoring three ions per compound (Table 2). The electron multiplier was set at a nominal value of 1612 V. 2.5. GC TQ-MS analysis The GC TQ-MS analysis was performed using a Thermo Trace 1310-Triple Quadrupole 8000 with autosampler IL 1310 from Thermo Scientific (San Jose, CA, USA). The temperatures of the transfer line, and the ion source were set at 290 and 350 ◦ C, respectively. The system was operated by Xcalibur 2.2 and Trace Finder TM 3.0. Separation was carried out on a TG-5 SILMS capillary column (30 m × 0.25 mm i.d., 0.25 m film thickness) obtained from Thermo Scientific (San Jose, CA, USA). Helium (purity 99.999%) was employed as carrier gas at a constant column flow of 1.0 mL min−1 . The GC oven temperature was programmed from 100 ◦ C (held 2 min) to 200 ◦ C at 20 ◦ C min−1 , to 260 ◦ C at 10 ◦ C min−1 and 20 ◦ C min−1 to 290 ◦ C. Splitless w/Surge mode was used for injection (200 kPa, held 1.2 min). After 1 min, the split valve was opened and the injector temperature was kept at 260 ◦ C. The injection
Table 4 ANOVA study: F ratios and p values obtained for ultrasound-assisted extraction (UAE). Fungicides
Metalaxyl Cyprodinil Iprovalicarb Myclobutanyl Benalaxyl Fenhexamide Tebuconazole Iprodione Dimethomorph
A: Solvent
B: Temperature (◦ C)
C: NaCl (%)
AB
AC
BC
F ratio
p Value
F ratio
p Value
F ratio
p Value
F ratio
p Value
F ratio
p Value
F ratio
p Value
14.31 55.67 76.80 9.03 54.54 137.85 261.57 16.58 47.71
0.02 0.004 0.002 0.04 0.004 0.001 0.000 0.02 0.005
0.43 6.20 13.73 0.35 0.40 2.57 1.53 0.26 0.44
0.56 0.08 0.03 0.60 0.57 0.21 0.30 0.65 0.55
0.14 0.00 1.98 0.66 12.26 0.01 16.58 4.39 0.08
0.73 0.99 0.25 0.48 0.04 0.93 0.03 0.13 0.80
0.30 2.60 5.40 0.70 5.90 3.20 3.05 0.87 1.51
0.83 0.23 0.10 0.61 0.09 0.18 0.19 0.54 0.37
5.99 4.18 6.84 0.84 4.92 5.47 1.20 1.52 3.39
0.09 0.14 0.07 0.55 0.11 0.09 0.44 0.37 0.17
0.40 1.05 2.78 0.35 0.48 0.85 1.53 0.01 1.46
0.57 0.38 0.19 0.60 0.54 0.43 0.30 0.94 0.31
p < 0.05 denotes statistical significance (indicated in bold).
M. Celeiro et al. / J. Chromatogr. A 1343 (2014) 18–25
21
volume was 1 L. The mass spectra detector (MSD) operated in selected reaction monitoring acquisition mode (SRM), monitoring two transitions per compound (Table 3). The electron multiplier was set at a nominal value of 1567 V.
TQ-MS/MS detector was operated in the SRM mode selecting two transitions per compound Tables 2 and 3.
2.6. Statistical analysis
The GC–MS instrument was employed for the optimization of the extraction procedure.
3.2. Optimization of the extraction process
Basic and descriptive statistics, as well as experimental design analysis were performed using Statgraphics-Plus v5.1 (Manugistics, Rockville, MD, USA) as software package. An experimental design was applied for the optimization of the extraction method, to analyze the simultaneous effect of the experimental parameters affecting UAE and PLE.
3.2.1. Ultrasound assisted extraction First efforts were focused on the development of an “easy to implement” low cost methodology based on the use of ultrasound energy. Ultrasound extraction employing an ultrasonic bath is a strategy affordable for any laboratory due to its low cost and simplicity of use. Most extraction optimization studies are carried out on spiked samples, implying that the real interaction of the sample with the analytes is not assessed. In the present study, a real non-spiked bagasse sample containing most target compounds was employed. The process was optimized by means of a multifactor experimental design 4 × 23 . Three factors were included: the extraction solvent, the temperature, and the addition of NaCl. The first factor was studied at four levels, and so the performance of four solvents was tested: ethyl acetate, hexane/acetone (1:1, v/v), methanol and hexane. The other two factors were studied at two
3. Results and discussion 3.1. Optimization of the chromatographic conditions GC–MS and GC TQ-MS The chromatographic conditions were optimized to achieve an efficient separation of the 11 target compounds (see conditions in section 2.5). For GC–MS analysis, the MS detector was operated in the SIM mode selecting three ions per compound and the GC
Metalaxyl
230 Hexane/acetone 190 MeOH
Hexane
110
3200
2200
Response (counts)
0
Hexane/acetone Ethyl Acetate Hexane MeOH
150
Ethyl Acetate Hexane MeOH
Response (counts)
Response (counts)
Hexane/acetone
180
100
Ethyl Acetate
MeOH
1500
Ethyl Acetate Hexane
1000 500
60
0
MeOH
Dimethomorph 500
Hexane
920 Hexane/acetone 720
520
Hexane/acetone
2000
90
1120
260
140
Hexane
2500
Iprodione
340
220
3000
210
120
MeOH
Fenhexamide
Hexane/acetone
180
Hexane
400
1200
Tebuconazole
300
Ethyl Acetate 600
200
Ethyl Acetate
MeOH
Response (counts)
Response (counts)
120
0
MeOH
Hexane/acetone
800
1700
240
30
Ethyl Acetate
1000
Benalaxyl
150
60
Hexane
2700
Myclobutanyl
90
1200
Hexane/acetone
Response (counts)
Ethyl Acetate
3700
Response (counts)
Response (counts)
Response (counts)
270
150
Iprovalicarb
Cyprodinil 4200
Hexane/acetone 400 300 200
Ethyl Acetate Hexane
100 MeOH
320
Fig. 1. UAE mean plot charts for the solvent.
0
22
M. Celeiro et al. / J. Chromatogr. A 1343 (2014) 18–25
NaCl
910
260
860
250
810
45ºC 25ºC
760 710 660
Response (counts)
Response (counts)
Temperature
240 230
20% NaCl 0% NaCl
220 210
Iprovalicarb
Tebuconazole
Fig. 2. UAE mean plot charts for temperature (iprovalicarb) and NaCl addition (tebuconazole).
levels: 25 and 45 ◦ C for the temperature; and 0 and 20% (w/v) for the NaCl. Other factors such as the amount of sample (0.5 g) and the extraction time (15 min) were maintained invariable. The results for the multifactor ANOVA study are shown in Table 4. As can be seen, the solvent was the most relevant factor being statistically significant for all analytes. The other factors, temperature and salt addition, were not significant. Fig. 1 shows the mean plot charts for the solvent. The use of methanol produces the lowest responses whereas hexane/acetone mixture provides maximum response. The mean plots for the other two factors are depicted in Fig. 2 for some representative compounds. As shown in the figure, better response is achieved at 25 ◦ C for iprovalicarb and without salt addition for tebuconazole. For the other compounds, those factors were not significant. The second order factors (interactions) were not significant in all cases. 3.2.2. Pressurized liquid extraction Under the optimal conditions, UAE was compared with PLE for the same real sample. PLE extractions were performed at 80 ◦ C for 15 min. Results are summarized in Fig. 3. Unexpectedly, the responses were clearly lower for UAE extraction and thus, we decided to continue the study using PLE. In these experiments, the sample size was 0.5 g and the extraction solvent hexane:acetone (1:1 v/v) since it was the most appropriate according to the previous study. Other two parameters which can drastically affect extraction, the PLE temperature (A) and time (B), were studied at three levels: 80, 100 and 120 ◦ C and 5, 10 and 15 min respectively, and optimized by means of an experimental design 23 . Once again, the study was performed using a real non-spiked bagasse sample.
The outcomes of the experimental design can be simply interpreted by visualizing several intuitive software tools provided by Statgraphics. In the Pareto charts (Fig. 4), the standardized effects are plotted in decreasing order of absolute magnitude, thus making easier to see which ones are the most important factors and interactions. In addition, the line drawn on the chart indicates whether an effect is statistically significant at a specified significance level (in this case, 95%). Main effect plots (Fig. 4) show how the response varies when each factor is changed from its low level to its high level, while all others factors are held at the centre of the experimental domain. In Fig. 4, the pareto charts and the main effect plots for the analytes showing significant effects are included. Temperature (A) was significant for four of the ten target analytes present in the sample. The quadratic term AA was also significant for metalaxyl and cyprodinil showing a maximum around 120 ◦ C. On the other hand, the time (B) was not significant for any of the compounds. Therefore, 5 min and 120 ◦ C were the experimental conditions selected. 3.3. Method performance The GC–MS and GC TQ-MS method performance parameters are summarized in Table 5. Regarding the instrumental linearity, methods exhibited a direct proportional relationship between the amount of each analyte and the chromatographic response. Calibration standards in hexane:acetone (1:1 v/v), were prepared covering a concentration range from 2 to 1000 ng mL−1 . Correlation coefficients R ≥ 0.993 for GC–MS analysis and R ≥ 0.998 for GC TQ-MS analysis were obtained. Method precision was studied within a day (n = 5) and among days (n = 9) at two concentration
8000
Response (counts)
7000
UAE
6000
PLE
5000 4000 3000 2000 1000 0
Fig. 3. Comparison between responses obtained by UAE and PLE.
M. Celeiro et al. / J. Chromatogr. A 1343 (2014) 18–25
Metalaxyl
Cyprodinil A
A AA
AB
AB
AA B
B
0
2
4
6
0
8
2
4
Fenhexamide
A
AA
B
AB
AB
B
AA 0
2
4
6
8
0
1
Cyprodinil 8200
8
10
12
2
3
4
Metalaxyl 1510
120ºC
120ºC
7700
1310
5 min
7200
15 min
6700 6200
5 min
1110
15 min
910
5700
80ºC
710
80ºC
Temperature
Temperature
Time
Fenhexamide
Time
Dimethomorph
(X 1000) 4,2
11900
120ºC
4
120ºC
10900
3,8
9900
5 min
15 min
8900 7900
6
Dimethomorph
A
5200
23
3,6 3,2
80ºC
5 min
3
80ºC
Temperature
15 min
3,4
Time
Temperature
Time
Fig. 4. PLE pareto charts and main effect plots for the analytes showing significant effects.
levels (20 ng mL−1 and 200 ng mL−1 ). For GC–MS analysis, RSD values ranged from 0.02 to 11% (intraday precision), and from 3.2 to 14% (inter-day precision). For GC TQ-MS, RSD values ranged from 0.59 to 12% (intraday precision), and from 3.3 to 13% (inter-day precision). Instrumental detection limits (IDLs) were calculated as the concentration giving a signal-to-noise ratio of three (S/N = 3). The obtained values were below 1 ng mL−1 for GC–MS analysis for the majority of the studied fungicides. For GC TQ-MS, they were much lower than for GC–MS, namely below 0.05 ng mL−1 (Table 5). Method quality parameters were evaluated using real bagasse samples and they are shown in Table 5, as well. Recovery studies were carried out by applying the optimized method to the extraction of a real sample, spiked at 100 ng g−1 and 1000 ng g−1 . Previous analyses of the samples showed the presence of some of the
target compounds and these initial concentrations were taken into account to calculate the recoveries. As can be seen in Table 5, recoveries were between 81 and 120% in all cases for GC–MS and GC TQ-MS. These recoveries can be considered quantitative and no matrix effects were observed. Therefore, quantification by external calibration can be effectively employed. The absence of matrix effect could be attributed to the fact that the proposed method does not require concentration. 0.5 g of sample is extracted with 20 mL of solvent and the extract is directly analyzed. On the contrary, most trace organic analytical methods very often require a drastic concentration step to achieve the desired or required LODs. In those cases, matrix effects often become a great problem. Precision was also evaluated and RSD values were generally lower than 12% for all fungicides. Limits of detection (LODs) and quantification (LOQs)
24
Table 5 Method quality parameters. Fungicides
GC–MS (SIM)
Recoveries, % (RSD)
GC TQ-MS (SRM)
Method detection and quantification limits
GC–MS (SIM) IDL (ng mL
0.9996 0.9998 0.9998 0.9996 0.9995 0.9995 0.9996 0.9930 0.9989 0.9992 0.9993
0.23 0.12 0.61 0.74 0.48 0.71 0.64 28.0 0.57 6.82 0.73
)
−1
Correlation coefficient (R)
IDL (ng mL
0.9995 0.9997 0.9988 0.9997 0.9997 0.9997 0.9997 0.9981 0.9999 0.9980 0.9996
0.013 0.011 0.007 0.049 0.017 0.004 0.004 0.033 0.011 0.010 0.015
)
−1
−1
−1
GC–MS (SIM) −1
−1
100 ng g
1000 ng g
100 ng g
1000 ng g
LOD (ng g
81 (5.6) 99 (6.3) 94 (4.1) 91 (5.8) 91 (6.7) 118 (7.7) 108 (11) – 105 (7.6) – 100 (12)
102 (10) 107 (9.7) 99 (12) 112 (10) 111 (6.7) 114 (12) 111 (9.1) – – 93 (16) 115 (9.9)
94 (8.0) 100 (9.9) 99 (8.6) 111 (7.1) 110 (11) 108 (8.8) 116 (12) 119 (5.9) 111 (9.6) – 104 (7.9)
100 (5.4) 96 (5.1) 97 (5.4) 118 (8.3) 114 (6.1) 97 (5.7) 105 (12) 111 (2.3) 120 (5.8) 107 (9.5) 104 (7.3)
9.20 4.80 24.4 29.6 19.2 28.4 25.6 1120 22.8 273 29.2
)
GC TQ-MS (SRM) −1
LOQ (ng g 30.4 15.8 80.5 97.7 63.4 93.7 84.5 3696 75.2 900 96.4
)
LOD (ng g−1 )
LOQ (ng g−1 )
0.52 0.44 0.28 1.96 0.68 0.16 0.16 1.32 0.44 0.40 0.60
1.72 1.45 0.92 6.47 2.24 0.52 0.52 4.36 1.45 1.32 1.98
Table 6 Analysis of white grape bagasse samples (ng g−1 ). Fungicides Samples Alb 01 Alb 02 Alb 03 Alb 04 Alb 05 Alb 06 Alb 07 Cai 01 Cai 02 Cai 03 Lou 01 Lou 02 Tre 01 Tre 02 Tre 03 God 01 God 02 God 03
Metalaxyl
Cyprodinil
Iprovalicarb
572
1007 155
390 873
239 899
12.5
206
658
Myclobutanyl
Benalaxyl
Fenhexamide
Tebuconazole
37.7
100 4.14 1427
141 5.85 130 1.56 9.14 5.37 39.4 2.39 15.3 1.50 5.39 2.28 1.64 5.54 32.7 11.3 4.05
203
87.7
81.2 45.9 261 3858
514 34.8 898
1049 9.42
1001
110 76.9 13.0 167 54.4 350.6 506
25.0 12.8 143.7 14.0 16.1
108
570
51.5 9.32 63.1
5.29 11.3 44.3 375 132 15.6 10.7
Iprodione
6021
10.9 3.19 15.8 6.30 85.9 52.4 8801 53.3
Dimethomorph 405 318 1698 2.65 8.69 13.7 26.1 235 67.0 2.74 12.3 2.58
41.4 2.03 13.9 27.2
M. Celeiro et al. / J. Chromatogr. A 1343 (2014) 18–25
Metalaxyl Cyprodinil Procymidone Iprovalicarb Myclobutanyl Kresoxim-methyl Benalaxyl Fenhexamide Tebuconazole Iprodione Dimethomorph
Correlation coefficient (R)
−1
GC TQ-MS (SRM)
M. Celeiro et al. / J. Chromatogr. A 1343 (2014) 18–25
were calculated as the concentration giving a signal-to-noise ratio of three (S/N) = 3 and 10 (S/N) = 10, respectively. For GC TQ-MS, LODs were in general below 1 ng g−1 and up to two orders of magnitude lower (even three orders for fenhexamide) than those obtained by GC–MS (see Table 5). Besides, LODs and LOQs were several orders of magnitude lower than the European MRLs (Table 1) for wine and table grapes and vine leaves. For GC–MS analysis, the obtained limits were also well below the wine and table grape MRLs although they were higher than those for vine leaves. In any case, we can conclude that the proposed method is highly sensitive, especially when TQ-MS detection is performed. It is important to emphasize that the PLE extract (20 mL) is directly analyzed without concentration and so if necessary, these limits could be even improved by concentrating the PLE extract. GC–MS and GC TQ-MS showed similar linearity, repeatability and reproducibility. Nevertheless, GC TQ-MS offered lower IDLs and LODs (about two orders of magnitude) improving selectivity and sensibility which is a great advantage to detect and quantify trace levels in real samples. 3.4. Application to real samples The validated method was applied to the analysis of 18 real white grape bagasse samples including five Galician varieties: ˜ (Cai), Loureira (Lou), Treixadura (Tre) and ˜ (Alb), Caino Albarino Godello (God). Results are shown in Table 6. The target fungicides were detected in all samples. Tebuconazole and dimethomorph were the most abundant (found in 17 and 16 samples, respectively). Fenhexamide and myclobutanyl were found in 72 and 67% of the samples, respectively. Metalaxyl, cyprodinil and iprodione were also detected in 9 of the 18 studied samples. Iprodione levels were quite high (6021 and 8800 ng g−1 ) but they were below the European maximum residue limits (MRLs) for wine grapes (the highest MRL among our target compounds). Iprovalicarb and benalaxyl were detected in 4 and 13 samples, respectively. Procymidone and kresoxim-methyl were not found. In general, Godello variety presented fewer fungicides (between 1 and 4) compared with the other 4 varieties. Fungicide concentration in all white grape bagasse samples were lower than the European maximum residue limits (MRLs) for wine grapes, excluding benalaxyl in a Treixadura sample (375 ng g−1 ). 4. Conclusions PLE has been successfully applied to the determination of 11 fungicides in white grape bagasse. To our knowledge, this study constitutes the first application of PLE–GC TQ-MS for the analysis of these compounds in bagasse samples. The most important parameters involved in the extraction were optimized using a multifactorial experimental design in real bagasse samples. Under the optimized conditions, fungicides were extracted with a mixture of hexane: acetone (1:1 v/v) for 5 min at 120 ◦ C. Method accuracy and precision were satisfactory, showing mean recovery values higher than 80% and RSD generally below 12%. We also compared the
25
GC–MS and GC TQ-MS techniques, the latter showing better IDLs and LODs than the former. In most cases, this difference was about two orders of magnitude, which undoubtedly provides an advantage for the detection of trace levels of fungicides in real bagasse samples. Acknowledgements This research was supported by FEDER funds and project CN 2012/299 (Xunta de Galicia, Spain). References [1] C.A. Damalas, I.G. Eleftherohorinos, Int. J. Environ. Res. Public Health 8 (2011) 1402. [2] R.C. Calhelha, J.V. Andrade, I.C. Ferreira, L.M. Estevinho, Food Microbiol. 23 (2006) 393. [3] Pesticide Action Network, Europe (PAN). Available at: http://www.pan-europe. info/Resources/Briefings/Message in a bottle (accessed 21.11.13). [4] R.M. Gonzalez-Rodriguez, B. Cancho-Grande, J. Simal-Gandara, J. Chromatogr. A 1216 (2009) 6033. [5] S. Romero, C. Domingo, R. Franquet, J. Garcia, Le Bulletin de l’O.I.V, 2007, pp. 241. [6] M. Lores, M. Iglesias-Estevez, M. Alvarez-Casas, M. Llompart, C. Garcia-Jares, Recursos Rurais, 8, IBADER, Santiago de Compostela, Spain, 2012, pp. 39. [7] Regulation (EC) No 396/2005 of the European Parliament and of the Council of 23 February 2005 on Maximum Residue Levels of Pesticides in or on Food and Feed of Plant and Animal Origin and Amending Council Directive 91/414/EEC L70/01. Available at: http://eur-lex.europa.eu/LexUriServ/ LexUriServ.do?uri=OJ:L:2005:070:0001:0016:en:PDF (accessed 25.11.13). [8] L. Lagunas-Allue, J. Sanz-Asensio, M.T. Martinez-Soria, Anal. Methods 3 (2011) 2881. [9] F. Dong, X. Chen, X. Liu, J. Xu, Y. Li, W. Shan, Y. Zheng, J. Chromatogr. A 1262 (2012) 98. [10] A.E. Afify, M.A. Mohamed, H.A. El-Gammal, E.R. Attallah, Int. J. Food Agric. Environ. 8 (2010) 602. [11] S.C.N. Queiroz, V.L. Ferracini, M.A. Rosa, Quimica Nova 35 (2012) 185. [12] X. Liu, X. Wang, J. Xu, F. Dong, W. Song, Y. Zheng, Biomed. Chromatogr. 25 (2011) 1081. [13] A. Bordagaray, R. Garcia-Arrona, E. Millan, Anal. Methods 5 (2013) 2565. [14] E.A.S. Silva, V. Lopez-Avila, J. Pawliszyn, J. Chromatogr. A 1313 (2013) 139. [15] M.G. Silva, A. Aquino, H.S. Doirea, S. Navickiene, Talanta 76 (2008) 680. [16] L. Lagunas-Allue, J. Sanz-Asensio, M.T. Martinez-Soria, Anal. Bioanal. Chem. 398 (2010) 1509. [17] S. Wang, Y. Xu, C. Pan, S. Jiang, F. Liu, Anal. Bioanal. Chem. 387 (2007) 673. [18] Association of Official Analytical Chemists, Official Methods of Analysis of the Association of Official Analytical Chemists, 1, 15th ed., Association of Official Analytical Chemists, 1990. [19] L. Lagunas-Allue, J. Sanz-Asensio, M.T. Martinez-Soria, J. Chromatogr. A 1270 (2012) 62. [20] P. Labarta, M.P. Martinez-Moral, M.T. Tena, Anal. Chem. 2012 (2012) 8. [21] E. Schreck, F. Geret, L. Gontier, M. Treilhou, Talanta 77 (2008) 298. [22] J.L.M. Vidal, J.A.P. Sanchez, P. Plaza-Bolaos, A.G. Frenich, R. Romero-Gonzalez, J. AOAC Int. 93 (2010) 1715. [23] T. Tanaka, T. Hori, T. Asada, K. Oikawa, K. Kawata, J. Chromatogr. A 1175 (2007) 181. [24] S.-K. Cho, A.M. Abd El-Aty, J.-H. Choi, Y.-M. Jeong, H.-C. Shin, B.-J. Chang, C. Lee, J.-H. Shim, J. Sep. Sci. 31 (2008) 1750. [25] S.J. Lehotay, T. Mary, G.A. Valverde, C. Mariano, M. Hans, H. Volkmar, A. Thomas, L. Gnter, F. Richard, M. Katerina, P. Mette Erecius, B. Amy, H. Walter, C. Jo Marie, A. Lutz, L. Karen, G. Miguel, H. Marvin, K. Andr de, H. Maurice, S. Frank, W. Anthony, P. Alesia, J. AOAC Int. 90 (2007) 485. [26] S. Walorczyk, D. Dro, J. AOAC Int. 94 (2011) 1625. [27] A.N. Brown, J.M. Cook, W.T. Hammack, J.S. Stepp, J.V. Pelt, G. Gerard, J. AOAC Int. 94 (2011) 931.
Contents lists available at ScienceDirect
Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma
Determination of fungicides in white grape bagasse by pressurized liquid extraction and gas chromatography tandem mass spectrometry夽 Maria Celeiro a , Maria Llompart a,∗ , J. Pablo Lamas a , Marta Lores a , Carmen Garcia-Jares a , Thierry Dagnac b a Department of Analytical Chemistry, Nutrition and Food Science, Faculty of Chemistry, Campus Vida, University of Santiago de Compostela, E-15782 Santiago de Compostela, Spain b Galician Institute for Food Quality (INGACAL), Agronomic and Agrarian Research Centre (CIAM), E-15080 A Coru˜ na, Spain
a r t i c l e
i n f o
Article history: Received 27 December 2013 Received in revised form 20 March 2014 Accepted 21 March 2014 Available online 28 March 2014 Keywords: Fungicides Bagasse Ultrasound-assisted extraction Pressurized liquid extraction GC–MS GC–TQ-MS
a b s t r a c t Ultrasound-assisted extraction (UAE) and pressurized liquid extraction (PLE) followed by gas chromatography–triple quadrupole-mass spectrometry (GC TQ-MS) were used for the rapid determination of 11 fungicides (metalaxyl, cyprodinil, procymidone, iprovalicarb, myclobutanyl, kresoxim-methyl, benalaxyl, fenhexamide, tebuconazole, iprodione and dimethomorph) in white grape bagasse. The extractions were optimized on real non-spiked samples by means of experimental design and the optimal conditions were selected to achieve the method validation. The PLE procedure showed much higher efficiency than UAE for the target fungicides. Under the selected extraction conditions, PLE showed satisfactory linearity, repeatability and reproducibility. Recoveries for the majority of studied fungicides were higher than 80% with relative standard deviations (RSD) lower than 12%. Limits of detection (LODs) for GC TQ-MS were very low, at the sub ng g−1 for the majority of the target fungicides, well below the European maximum residue limits (MRLs) for wine and table grapes, and vine leaves. Eighteen white grape bagasse samples were analyzed and nine out of eleven targets were detected in the samples. Seven of them were detected in more than 50% of the samples and most samples contained at least four of the target analytes. The most frequently found compounds were tebuconazole and dimethomorph with concentrations between 1.6–130 and 2.0–1788 ng g−1 , respectively. Some samples showed high levels of many of the studied fungicides (high ng g−1 , even g g−1 for cyprodinil, fenhexamide, iprodione and dimethomorph), but all of them below the European maximum residue limits (MRLs) for wine grapes. © 2014 Elsevier B.V. All rights reserved.
1. Introduction There is an increasing interest regarding health and safety aspects associated with the use of pesticides and the presence of their residues in food and drinks. Pesticides are used to control pests in vegetables, fruits or cereal grains among others [1]. Fungicides are a class of pesticides widespread used in viticulture to avoid fungi infection of Vitis plants, being mainly used for treating grey rot (Botrytis cinerea), mildew (Plasmopara viticola) and oidium (Uncinula necator). Their use has brought many
夽 Presented at the XIII Scientific Meeting of the Spanish Society of Chromatography and Related Techniques (SECyTA2013), 8–11 October 2013, Puerto de la Cruz, Tenerife, Canary Islands, Spain. ∗ Corresponding author. Tel.: +34 881814225; fax: +34 981595012. E-mail address: [email protected] (M. Llompart). http://dx.doi.org/10.1016/j.chroma.2014.03.057 0021-9673/© 2014 Elsevier B.V. All rights reserved.
benefits with respect to enhanced quality of produced crops, but there are concerns about the presence of their residues in crops, which may pose a health hazard to the consumers. In addition, several studies have shown that some fungicides and their degradation products can be found in musts and some of them are frequently found at low concentration levels in the final commercial wine [2–5]. Bagasse (also called marc) is the residue left behind after the juice has been removed from bunch of grapes during winemaking. There is an increasing interest, supported by environmental and economic reasons, to recover and exploit these wastes from the food industry, because such residues can be used as a source of natural bioactive compounds, which could in turn be used in pharmaceutical, cosmetics or back in the food industry. Therefore, the levels of fungicides in grape bagasse must be controlled in order to avoid environmental pollution and human exposure to these compounds [6].
M. Celeiro et al. / J. Chromatogr. A 1343 (2014) 18–25 Table 1 European maximum residue limits (MRLs) for wine and table grapes and vine leaves [7]. Fungicide Metalaxyl Cyprodinil Procymidone Iprovalicarb Myclobutanyl Kresoxim-methyl Benalaxyl Fenhexamide Tebuconazole Iprodione Dimethomorph
Table grapes (mg kg−1 )
Wine grapes (mg kg−1 ) 1 5 0.01 2 1 1 0.3 5 2 10 3
Vine leaves (mg kg−1 )
2 5 0.01 2 1 1 0.3 5 2 10 3
0.05 0.05 0.01 0.05 0.02 0.05 0.05 0.05 0.05 0.02 0.01
The European Community establishes the maximum residue limits (MRLs) for different fungicides in wine and table grapes as well as in vine leaves through EC Regulation 396/2005 and their subsequent amendments [7], but not any harmonized MRLs have been laid down in the European Union for pesticides in bagasse or wine. European MRLs set for the target fungicides in wine and table grapes, and in vine leaves are shown in Table 1. Nevertheless, a look at the scientific literature evidences the lack of studies devoted to the development of methodology for the determination of fungicides in bagasse samples whereas in grapes, other fruits and vegetables, environmental friendly procedures including microwave assisted extraction (MAE) [8], QuEChERS [9–12], solid phase micro-extraction (SPME) [13,14] or matrix–solid phase dispersion (MSPD) [15–17] are substituting traditional methodologies like Soxhlet extraction [18,19]. Pressurized liquid extraction (PLE) was also employed to determine various chemical classes of fungicides in different matrices such as mushroom compost [20], vineyard and agricultural soils [21,22], green leafy vegetables [23] or green tea [24]. The analytical methods for detecting and quantifying fungicides in different fruits are generally based on liquid chromatography (LC) or gas chromatography (GC). The decision to use either LC or GC is based on the physico-chemical properties of the target analytes. The on-line coupling of efficient liquid chromatography or gas chromatography separation with mass spectrometry detection (LC–MS or GC–MS) has became an accepted technique for performing regulatory monitoring. GC–MS is an advantageous and powerful technique for the determination of (semi)volatile and low polarity fungicides in vegetable samples and LC–MS is the best choice for substances with low volatility and/or thermal instability [25]. Liquid or gas chromatography in combination with tandem mass spectrometry (MS–MS) is a valuable approach that improves selectivity and analyte sensitivity, minimizing most of the matrix interferences. In this way, triple quadrupole (TQ) working under
19
MS/MS mode can achieve lower limits of detection than simple quadrupole GC–SQ–MS or LC–SQ–MS and increase the selectivity and specificity of the method. LC TQ-MS was successfully employed to determine fungicides in cereals, vegetables and fruits [9], grapes [10] or food (lettuce, tomato, apple and grapes) [11] and although there are few references of GC TQ-MS to determine fungicides in vegetables or fruit samples, this technique was recently used to analyze more than 140 pesticides in vegetables [26,27]. The aim of this study was to develop and validate a method to analyze 11 fungicides from different chemical classes (metalaxyl, cyprodinil, procymidone, iprovalicarb, myclobutanyl, kresoximmethyl, benalaxyl, fenhexamide, tebuconazole, iprodione and dimethomorph) in white grape bagasse, based on pressurized liquid extraction-gas chromatography–triple quadrupole-mass spectrometry. This study also aimed at comparing GC–TQ–MS with GC–SQ–MS to determine whether the use of the former technique is an improvement for the detection of the target fungicides. Finally, the validated method was used to identify and quantify the studied fungicides in real white grape bagasse. To the best of our knowledge, this is the first time that PLE–GC TQ-MS is applied to the analysis of fungicides in bagasse samples. 2. Materials and methods 2.1. Chemicals, materials and samples The studied compounds, their chemical names, CAS numbers, suppliers and purity are summarized in Table 2. Acetone, ethyl acetate and n-hexane were provided by SigmaAldrich Chemie GmbH (Steinheim, Germany). Methanol was provided by Merk (Darmstadt, Germany). Sand (200–300 m) was purchased from Scharlau (Barcelona, Spain) and sodium chloride (NaCl) was provided by Prolabo (Leuven, Belgium). Individual stock solutions of each compound were prepared in methanol or ethyl acetate. Further dilutions and mixtures were prepared in acetone (sample fortification solutions) and hexane/acetone (calibration standards). All solutions were stored in amber glass vials at −20 ◦ C. All solvents and reagents were of analytical grade. Bagasse samples were selected among five different varieties of ˜ Caino, ˜ Loureira, Treixadura and white grape of Galicia (Albarino, Godello). Samples were dried at 60 ◦ C for 24 h before use, and subsequently were crushed in a conventional coffee grinder and pulverized in a porcelain mortar using a porcelain pestle until a homogeneous mixture was obtained (ca. 5 min). 2.2. UAE procedure Ultrasound-assisted extraction (UAE) was carried out using an ultrasonic cleaning bath with a working frequency of 50 kHz and
Table 2 Target compounds: purity, suppliers, CAS numbers, and GC–MS parameters. Fungicides
Purity (%)a
CAS
Retention time (min)
Quantification and identification ionsb
Metalaxyl Cyprodinil Procymidone Iprovalicarb Myclobutanyl Kresoxim-methyl Benalaxyl Fenhexamide Tebuconazole Iprodione Dimethomorph
99.5 97.5 99.5 99 97.5 98 98 99 99 97.5 98
70630-17-0 32809-16-8 88671-89-0 140923-17-7 143390-89-0 71626-11-4 121552-61-2 126833-17-8 107534-96-3 36734-19-7 110488-70-5
9.67 10.77 11.07 11.74/11.90 11.95 11.89 12.94 13.28 13.47 13.80 18.87/19.43
192.1 (42), 160.1 (57), 206.1 (100) 210.1 (10), 224.0 (100), 225.1 (61) 96.0 (203), 283 (100), 285 (65) 116.1 (99), 119.0 (71), 134.0 (100) 150.0 (51), 179.0 (100), 245.0 (45) 116 (214), 131 (100), 206.1 (92) 91.0 (45), 148.1 (100), 206.1 (26) 55.0 (40), 97.0 (100), 177.0 (34) 83.0 (84), 125 (145), 250.0 (100) 186.9 (51), 244.8 (20), 313.9 (100) 165.0 (32), 301.0 (100), 387.1 (29)
a b
Dr. Ehrenstorfer (Ausburg, Germany). Numbers in brackets are the relative ion abundances, %.
20
M. Celeiro et al. / J. Chromatogr. A 1343 (2014) 18–25
Table 3 GC TQ-MS retention time and selected transitions. Fungicides
Retention time (min)
Precursor iona
Product iona
Collision energya
Metalaxyl Cyprodinil Procymidone Iprovalicarb Myclobutanyl Kresoxim-methyl Benalaxyl Fenhexamide Tebuconazole Iprodione Dimethomorph
11.62 12.91 13.27 14.04 14.26 14.20 15.36 15.78 16.02 16.58 23.92/24.60
206.1,234.1 224.1,225.1 283.0,283.0 116.0,116.0 179.1,179.1 206.1,206.1 234.1,266.1 177.0,301.1 250.1,252.1 314.0, 316.0 301.1,387.1
132.0,174.1 208.1,210.1 96.0,255.0 55.1,98.0 125.0,152.1 131.1,116.1 174.1,148.1 113.1,97.0 125.1,127.1 245.0, 247.0 165.1,301.1
10,10 20,18 15,10 10,15 15,15 15,15 10,10 10,15 20,20 15, 15 10,12
a
Underlined values are the quantification transitions.
110 W of power (Ultrasound Med-II, J.P. Selecta, Barcelona, Spain). 0.5 g of bagasse were mixed with 5 mL of the appropriate extraction solvent (ethyl acetate, hexane:acetone (1:1 v/v), methanol or hexane) in a 10 mL vial that was placed in the ultrasound bath. The mixture bagasse sample-solvent was sonicated (15 min) at different temperatures (25 or 45 ◦ C) and with NaCl (0 or 20% w/v). Afterwards, the supernatants were filtered through 0.45-m PTFE microporous filters (25 mm diameter), evaporated under a gentle nitrogen stream and reconstituted in ethyl acetate. The extracts, diluted when necessary, were analyzed by GC–MS and GC TQ-MS. 2.3. PLE procedure Extractions were performed on an ASE 150 (Dionex, Co., Sunyvale, CA, USA), equipped with 10 mL stainless steel cells and 60 mL collection vials. Two cellulose filters (Dionex) were placed at each end of the PLE cell. 0.5 g of sample were weighted and introduced into the cell, where previously 1 g of clean sand (200–300 m mesh particle size, Scharlau, Barcelona, Spain) was placed. For the preparation of fortified samples, the sample bagasse was spiked with 10 L of the corresponding acetone solution of the target compounds to get the desired final concentration. Finally, the dead volume of the cell was filled with sand. The cell was tightly closed and placed into PLE system. Extractions were performed by preheating the cell before filling with solvent (preheat method). The extraction pressure was 1500 psi, the flush volume was 60%, and the purge time 60 s. Hexane:acetone (1:1, v/v) was employed as extraction mixture. Three temperatures were studied (80, 100 and 120 ◦ C) at 5, 10 and 15 min. In all cases, the extracts were leveled to a final volume of 20 mL and were analyzed by GC–MS and GC TQ-MS. 2.4. GC–MS analysis The GC–MS analysis was performed using an Agilent 7890A (GC)-Agilent 5975C inert MSD with triple axis detector and an
Agilent 7693 autosampler from Agilent Technologies (Palo Alto, CA, USA). The temperatures of the transfer line, the quadrupole and the ion source were set at 290, 150 and 230 ◦ C, respectively. The system was operated by Agilent MSD ChemStation E.02.00.493 software. Separation was carried out on a cross-linked 5%phenyl polysilphenylen-siloxane TR-5 MS capillary column (30 m × 0.25 mm i.d., 0.25 m film thickness) obtained from Thermo Scientific (Palo Alto, CA, USA). Helium (purity 99.999%) was employed as carrier gas at a constant column flow of 1.0 mL min−1 . The GC oven temperature was programmed from 100 ◦ C (held 2 min) to 200 ◦ C at 20 ◦ C min−1 , to 260 ◦ C at 10 ◦ C min−1 and 20 ◦ C min−1 to 290 ◦ C. Pulsed splitless mode was used for injection (30 psi, held 1.2 min). After 1 min, the split valve was opened and the injector temperature was kept at 260 ◦ C. The injection volume was 1 L. The mass spectra detector (MSD) operated in selected ion monitoring (SIM) mode, monitoring three ions per compound (Table 2). The electron multiplier was set at a nominal value of 1612 V. 2.5. GC TQ-MS analysis The GC TQ-MS analysis was performed using a Thermo Trace 1310-Triple Quadrupole 8000 with autosampler IL 1310 from Thermo Scientific (San Jose, CA, USA). The temperatures of the transfer line, and the ion source were set at 290 and 350 ◦ C, respectively. The system was operated by Xcalibur 2.2 and Trace Finder TM 3.0. Separation was carried out on a TG-5 SILMS capillary column (30 m × 0.25 mm i.d., 0.25 m film thickness) obtained from Thermo Scientific (San Jose, CA, USA). Helium (purity 99.999%) was employed as carrier gas at a constant column flow of 1.0 mL min−1 . The GC oven temperature was programmed from 100 ◦ C (held 2 min) to 200 ◦ C at 20 ◦ C min−1 , to 260 ◦ C at 10 ◦ C min−1 and 20 ◦ C min−1 to 290 ◦ C. Splitless w/Surge mode was used for injection (200 kPa, held 1.2 min). After 1 min, the split valve was opened and the injector temperature was kept at 260 ◦ C. The injection
Table 4 ANOVA study: F ratios and p values obtained for ultrasound-assisted extraction (UAE). Fungicides
Metalaxyl Cyprodinil Iprovalicarb Myclobutanyl Benalaxyl Fenhexamide Tebuconazole Iprodione Dimethomorph
A: Solvent
B: Temperature (◦ C)
C: NaCl (%)
AB
AC
BC
F ratio
p Value
F ratio
p Value
F ratio
p Value
F ratio
p Value
F ratio
p Value
F ratio
p Value
14.31 55.67 76.80 9.03 54.54 137.85 261.57 16.58 47.71
0.02 0.004 0.002 0.04 0.004 0.001 0.000 0.02 0.005
0.43 6.20 13.73 0.35 0.40 2.57 1.53 0.26 0.44
0.56 0.08 0.03 0.60 0.57 0.21 0.30 0.65 0.55
0.14 0.00 1.98 0.66 12.26 0.01 16.58 4.39 0.08
0.73 0.99 0.25 0.48 0.04 0.93 0.03 0.13 0.80
0.30 2.60 5.40 0.70 5.90 3.20 3.05 0.87 1.51
0.83 0.23 0.10 0.61 0.09 0.18 0.19 0.54 0.37
5.99 4.18 6.84 0.84 4.92 5.47 1.20 1.52 3.39
0.09 0.14 0.07 0.55 0.11 0.09 0.44 0.37 0.17
0.40 1.05 2.78 0.35 0.48 0.85 1.53 0.01 1.46
0.57 0.38 0.19 0.60 0.54 0.43 0.30 0.94 0.31
p < 0.05 denotes statistical significance (indicated in bold).
M. Celeiro et al. / J. Chromatogr. A 1343 (2014) 18–25
21
volume was 1 L. The mass spectra detector (MSD) operated in selected reaction monitoring acquisition mode (SRM), monitoring two transitions per compound (Table 3). The electron multiplier was set at a nominal value of 1567 V.
TQ-MS/MS detector was operated in the SRM mode selecting two transitions per compound Tables 2 and 3.
2.6. Statistical analysis
The GC–MS instrument was employed for the optimization of the extraction procedure.
3.2. Optimization of the extraction process
Basic and descriptive statistics, as well as experimental design analysis were performed using Statgraphics-Plus v5.1 (Manugistics, Rockville, MD, USA) as software package. An experimental design was applied for the optimization of the extraction method, to analyze the simultaneous effect of the experimental parameters affecting UAE and PLE.
3.2.1. Ultrasound assisted extraction First efforts were focused on the development of an “easy to implement” low cost methodology based on the use of ultrasound energy. Ultrasound extraction employing an ultrasonic bath is a strategy affordable for any laboratory due to its low cost and simplicity of use. Most extraction optimization studies are carried out on spiked samples, implying that the real interaction of the sample with the analytes is not assessed. In the present study, a real non-spiked bagasse sample containing most target compounds was employed. The process was optimized by means of a multifactor experimental design 4 × 23 . Three factors were included: the extraction solvent, the temperature, and the addition of NaCl. The first factor was studied at four levels, and so the performance of four solvents was tested: ethyl acetate, hexane/acetone (1:1, v/v), methanol and hexane. The other two factors were studied at two
3. Results and discussion 3.1. Optimization of the chromatographic conditions GC–MS and GC TQ-MS The chromatographic conditions were optimized to achieve an efficient separation of the 11 target compounds (see conditions in section 2.5). For GC–MS analysis, the MS detector was operated in the SIM mode selecting three ions per compound and the GC
Metalaxyl
230 Hexane/acetone 190 MeOH
Hexane
110
3200
2200
Response (counts)
0
Hexane/acetone Ethyl Acetate Hexane MeOH
150
Ethyl Acetate Hexane MeOH
Response (counts)
Response (counts)
Hexane/acetone
180
100
Ethyl Acetate
MeOH
1500
Ethyl Acetate Hexane
1000 500
60
0
MeOH
Dimethomorph 500
Hexane
920 Hexane/acetone 720
520
Hexane/acetone
2000
90
1120
260
140
Hexane
2500
Iprodione
340
220
3000
210
120
MeOH
Fenhexamide
Hexane/acetone
180
Hexane
400
1200
Tebuconazole
300
Ethyl Acetate 600
200
Ethyl Acetate
MeOH
Response (counts)
Response (counts)
120
0
MeOH
Hexane/acetone
800
1700
240
30
Ethyl Acetate
1000
Benalaxyl
150
60
Hexane
2700
Myclobutanyl
90
1200
Hexane/acetone
Response (counts)
Ethyl Acetate
3700
Response (counts)
Response (counts)
Response (counts)
270
150
Iprovalicarb
Cyprodinil 4200
Hexane/acetone 400 300 200
Ethyl Acetate Hexane
100 MeOH
320
Fig. 1. UAE mean plot charts for the solvent.
0
22
M. Celeiro et al. / J. Chromatogr. A 1343 (2014) 18–25
NaCl
910
260
860
250
810
45ºC 25ºC
760 710 660
Response (counts)
Response (counts)
Temperature
240 230
20% NaCl 0% NaCl
220 210
Iprovalicarb
Tebuconazole
Fig. 2. UAE mean plot charts for temperature (iprovalicarb) and NaCl addition (tebuconazole).
levels: 25 and 45 ◦ C for the temperature; and 0 and 20% (w/v) for the NaCl. Other factors such as the amount of sample (0.5 g) and the extraction time (15 min) were maintained invariable. The results for the multifactor ANOVA study are shown in Table 4. As can be seen, the solvent was the most relevant factor being statistically significant for all analytes. The other factors, temperature and salt addition, were not significant. Fig. 1 shows the mean plot charts for the solvent. The use of methanol produces the lowest responses whereas hexane/acetone mixture provides maximum response. The mean plots for the other two factors are depicted in Fig. 2 for some representative compounds. As shown in the figure, better response is achieved at 25 ◦ C for iprovalicarb and without salt addition for tebuconazole. For the other compounds, those factors were not significant. The second order factors (interactions) were not significant in all cases. 3.2.2. Pressurized liquid extraction Under the optimal conditions, UAE was compared with PLE for the same real sample. PLE extractions were performed at 80 ◦ C for 15 min. Results are summarized in Fig. 3. Unexpectedly, the responses were clearly lower for UAE extraction and thus, we decided to continue the study using PLE. In these experiments, the sample size was 0.5 g and the extraction solvent hexane:acetone (1:1 v/v) since it was the most appropriate according to the previous study. Other two parameters which can drastically affect extraction, the PLE temperature (A) and time (B), were studied at three levels: 80, 100 and 120 ◦ C and 5, 10 and 15 min respectively, and optimized by means of an experimental design 23 . Once again, the study was performed using a real non-spiked bagasse sample.
The outcomes of the experimental design can be simply interpreted by visualizing several intuitive software tools provided by Statgraphics. In the Pareto charts (Fig. 4), the standardized effects are plotted in decreasing order of absolute magnitude, thus making easier to see which ones are the most important factors and interactions. In addition, the line drawn on the chart indicates whether an effect is statistically significant at a specified significance level (in this case, 95%). Main effect plots (Fig. 4) show how the response varies when each factor is changed from its low level to its high level, while all others factors are held at the centre of the experimental domain. In Fig. 4, the pareto charts and the main effect plots for the analytes showing significant effects are included. Temperature (A) was significant for four of the ten target analytes present in the sample. The quadratic term AA was also significant for metalaxyl and cyprodinil showing a maximum around 120 ◦ C. On the other hand, the time (B) was not significant for any of the compounds. Therefore, 5 min and 120 ◦ C were the experimental conditions selected. 3.3. Method performance The GC–MS and GC TQ-MS method performance parameters are summarized in Table 5. Regarding the instrumental linearity, methods exhibited a direct proportional relationship between the amount of each analyte and the chromatographic response. Calibration standards in hexane:acetone (1:1 v/v), were prepared covering a concentration range from 2 to 1000 ng mL−1 . Correlation coefficients R ≥ 0.993 for GC–MS analysis and R ≥ 0.998 for GC TQ-MS analysis were obtained. Method precision was studied within a day (n = 5) and among days (n = 9) at two concentration
8000
Response (counts)
7000
UAE
6000
PLE
5000 4000 3000 2000 1000 0
Fig. 3. Comparison between responses obtained by UAE and PLE.
M. Celeiro et al. / J. Chromatogr. A 1343 (2014) 18–25
Metalaxyl
Cyprodinil A
A AA
AB
AB
AA B
B
0
2
4
6
0
8
2
4
Fenhexamide
A
AA
B
AB
AB
B
AA 0
2
4
6
8
0
1
Cyprodinil 8200
8
10
12
2
3
4
Metalaxyl 1510
120ºC
120ºC
7700
1310
5 min
7200
15 min
6700 6200
5 min
1110
15 min
910
5700
80ºC
710
80ºC
Temperature
Temperature
Time
Fenhexamide
Time
Dimethomorph
(X 1000) 4,2
11900
120ºC
4
120ºC
10900
3,8
9900
5 min
15 min
8900 7900
6
Dimethomorph
A
5200
23
3,6 3,2
80ºC
5 min
3
80ºC
Temperature
15 min
3,4
Time
Temperature
Time
Fig. 4. PLE pareto charts and main effect plots for the analytes showing significant effects.
levels (20 ng mL−1 and 200 ng mL−1 ). For GC–MS analysis, RSD values ranged from 0.02 to 11% (intraday precision), and from 3.2 to 14% (inter-day precision). For GC TQ-MS, RSD values ranged from 0.59 to 12% (intraday precision), and from 3.3 to 13% (inter-day precision). Instrumental detection limits (IDLs) were calculated as the concentration giving a signal-to-noise ratio of three (S/N = 3). The obtained values were below 1 ng mL−1 for GC–MS analysis for the majority of the studied fungicides. For GC TQ-MS, they were much lower than for GC–MS, namely below 0.05 ng mL−1 (Table 5). Method quality parameters were evaluated using real bagasse samples and they are shown in Table 5, as well. Recovery studies were carried out by applying the optimized method to the extraction of a real sample, spiked at 100 ng g−1 and 1000 ng g−1 . Previous analyses of the samples showed the presence of some of the
target compounds and these initial concentrations were taken into account to calculate the recoveries. As can be seen in Table 5, recoveries were between 81 and 120% in all cases for GC–MS and GC TQ-MS. These recoveries can be considered quantitative and no matrix effects were observed. Therefore, quantification by external calibration can be effectively employed. The absence of matrix effect could be attributed to the fact that the proposed method does not require concentration. 0.5 g of sample is extracted with 20 mL of solvent and the extract is directly analyzed. On the contrary, most trace organic analytical methods very often require a drastic concentration step to achieve the desired or required LODs. In those cases, matrix effects often become a great problem. Precision was also evaluated and RSD values were generally lower than 12% for all fungicides. Limits of detection (LODs) and quantification (LOQs)
24
Table 5 Method quality parameters. Fungicides
GC–MS (SIM)
Recoveries, % (RSD)
GC TQ-MS (SRM)
Method detection and quantification limits
GC–MS (SIM) IDL (ng mL
0.9996 0.9998 0.9998 0.9996 0.9995 0.9995 0.9996 0.9930 0.9989 0.9992 0.9993
0.23 0.12 0.61 0.74 0.48 0.71 0.64 28.0 0.57 6.82 0.73
)
−1
Correlation coefficient (R)
IDL (ng mL
0.9995 0.9997 0.9988 0.9997 0.9997 0.9997 0.9997 0.9981 0.9999 0.9980 0.9996
0.013 0.011 0.007 0.049 0.017 0.004 0.004 0.033 0.011 0.010 0.015
)
−1
−1
−1
GC–MS (SIM) −1
−1
100 ng g
1000 ng g
100 ng g
1000 ng g
LOD (ng g
81 (5.6) 99 (6.3) 94 (4.1) 91 (5.8) 91 (6.7) 118 (7.7) 108 (11) – 105 (7.6) – 100 (12)
102 (10) 107 (9.7) 99 (12) 112 (10) 111 (6.7) 114 (12) 111 (9.1) – – 93 (16) 115 (9.9)
94 (8.0) 100 (9.9) 99 (8.6) 111 (7.1) 110 (11) 108 (8.8) 116 (12) 119 (5.9) 111 (9.6) – 104 (7.9)
100 (5.4) 96 (5.1) 97 (5.4) 118 (8.3) 114 (6.1) 97 (5.7) 105 (12) 111 (2.3) 120 (5.8) 107 (9.5) 104 (7.3)
9.20 4.80 24.4 29.6 19.2 28.4 25.6 1120 22.8 273 29.2
)
GC TQ-MS (SRM) −1
LOQ (ng g 30.4 15.8 80.5 97.7 63.4 93.7 84.5 3696 75.2 900 96.4
)
LOD (ng g−1 )
LOQ (ng g−1 )
0.52 0.44 0.28 1.96 0.68 0.16 0.16 1.32 0.44 0.40 0.60
1.72 1.45 0.92 6.47 2.24 0.52 0.52 4.36 1.45 1.32 1.98
Table 6 Analysis of white grape bagasse samples (ng g−1 ). Fungicides Samples Alb 01 Alb 02 Alb 03 Alb 04 Alb 05 Alb 06 Alb 07 Cai 01 Cai 02 Cai 03 Lou 01 Lou 02 Tre 01 Tre 02 Tre 03 God 01 God 02 God 03
Metalaxyl
Cyprodinil
Iprovalicarb
572
1007 155
390 873
239 899
12.5
206
658
Myclobutanyl
Benalaxyl
Fenhexamide
Tebuconazole
37.7
100 4.14 1427
141 5.85 130 1.56 9.14 5.37 39.4 2.39 15.3 1.50 5.39 2.28 1.64 5.54 32.7 11.3 4.05
203
87.7
81.2 45.9 261 3858
514 34.8 898
1049 9.42
1001
110 76.9 13.0 167 54.4 350.6 506
25.0 12.8 143.7 14.0 16.1
108
570
51.5 9.32 63.1
5.29 11.3 44.3 375 132 15.6 10.7
Iprodione
6021
10.9 3.19 15.8 6.30 85.9 52.4 8801 53.3
Dimethomorph 405 318 1698 2.65 8.69 13.7 26.1 235 67.0 2.74 12.3 2.58
41.4 2.03 13.9 27.2
M. Celeiro et al. / J. Chromatogr. A 1343 (2014) 18–25
Metalaxyl Cyprodinil Procymidone Iprovalicarb Myclobutanyl Kresoxim-methyl Benalaxyl Fenhexamide Tebuconazole Iprodione Dimethomorph
Correlation coefficient (R)
−1
GC TQ-MS (SRM)
M. Celeiro et al. / J. Chromatogr. A 1343 (2014) 18–25
were calculated as the concentration giving a signal-to-noise ratio of three (S/N) = 3 and 10 (S/N) = 10, respectively. For GC TQ-MS, LODs were in general below 1 ng g−1 and up to two orders of magnitude lower (even three orders for fenhexamide) than those obtained by GC–MS (see Table 5). Besides, LODs and LOQs were several orders of magnitude lower than the European MRLs (Table 1) for wine and table grapes and vine leaves. For GC–MS analysis, the obtained limits were also well below the wine and table grape MRLs although they were higher than those for vine leaves. In any case, we can conclude that the proposed method is highly sensitive, especially when TQ-MS detection is performed. It is important to emphasize that the PLE extract (20 mL) is directly analyzed without concentration and so if necessary, these limits could be even improved by concentrating the PLE extract. GC–MS and GC TQ-MS showed similar linearity, repeatability and reproducibility. Nevertheless, GC TQ-MS offered lower IDLs and LODs (about two orders of magnitude) improving selectivity and sensibility which is a great advantage to detect and quantify trace levels in real samples. 3.4. Application to real samples The validated method was applied to the analysis of 18 real white grape bagasse samples including five Galician varieties: ˜ (Cai), Loureira (Lou), Treixadura (Tre) and ˜ (Alb), Caino Albarino Godello (God). Results are shown in Table 6. The target fungicides were detected in all samples. Tebuconazole and dimethomorph were the most abundant (found in 17 and 16 samples, respectively). Fenhexamide and myclobutanyl were found in 72 and 67% of the samples, respectively. Metalaxyl, cyprodinil and iprodione were also detected in 9 of the 18 studied samples. Iprodione levels were quite high (6021 and 8800 ng g−1 ) but they were below the European maximum residue limits (MRLs) for wine grapes (the highest MRL among our target compounds). Iprovalicarb and benalaxyl were detected in 4 and 13 samples, respectively. Procymidone and kresoxim-methyl were not found. In general, Godello variety presented fewer fungicides (between 1 and 4) compared with the other 4 varieties. Fungicide concentration in all white grape bagasse samples were lower than the European maximum residue limits (MRLs) for wine grapes, excluding benalaxyl in a Treixadura sample (375 ng g−1 ). 4. Conclusions PLE has been successfully applied to the determination of 11 fungicides in white grape bagasse. To our knowledge, this study constitutes the first application of PLE–GC TQ-MS for the analysis of these compounds in bagasse samples. The most important parameters involved in the extraction were optimized using a multifactorial experimental design in real bagasse samples. Under the optimized conditions, fungicides were extracted with a mixture of hexane: acetone (1:1 v/v) for 5 min at 120 ◦ C. Method accuracy and precision were satisfactory, showing mean recovery values higher than 80% and RSD generally below 12%. We also compared the
25
GC–MS and GC TQ-MS techniques, the latter showing better IDLs and LODs than the former. In most cases, this difference was about two orders of magnitude, which undoubtedly provides an advantage for the detection of trace levels of fungicides in real bagasse samples. Acknowledgements This research was supported by FEDER funds and project CN 2012/299 (Xunta de Galicia, Spain). References [1] C.A. Damalas, I.G. Eleftherohorinos, Int. J. Environ. Res. Public Health 8 (2011) 1402. [2] R.C. Calhelha, J.V. Andrade, I.C. Ferreira, L.M. Estevinho, Food Microbiol. 23 (2006) 393. [3] Pesticide Action Network, Europe (PAN). Available at: http://www.pan-europe. info/Resources/Briefings/Message in a bottle (accessed 21.11.13). [4] R.M. Gonzalez-Rodriguez, B. Cancho-Grande, J. Simal-Gandara, J. Chromatogr. A 1216 (2009) 6033. [5] S. Romero, C. Domingo, R. Franquet, J. Garcia, Le Bulletin de l’O.I.V, 2007, pp. 241. [6] M. Lores, M. Iglesias-Estevez, M. Alvarez-Casas, M. Llompart, C. Garcia-Jares, Recursos Rurais, 8, IBADER, Santiago de Compostela, Spain, 2012, pp. 39. [7] Regulation (EC) No 396/2005 of the European Parliament and of the Council of 23 February 2005 on Maximum Residue Levels of Pesticides in or on Food and Feed of Plant and Animal Origin and Amending Council Directive 91/414/EEC L70/01. Available at: http://eur-lex.europa.eu/LexUriServ/ LexUriServ.do?uri=OJ:L:2005:070:0001:0016:en:PDF (accessed 25.11.13). [8] L. Lagunas-Allue, J. Sanz-Asensio, M.T. Martinez-Soria, Anal. Methods 3 (2011) 2881. [9] F. Dong, X. Chen, X. Liu, J. Xu, Y. Li, W. Shan, Y. Zheng, J. Chromatogr. A 1262 (2012) 98. [10] A.E. Afify, M.A. Mohamed, H.A. El-Gammal, E.R. Attallah, Int. J. Food Agric. Environ. 8 (2010) 602. [11] S.C.N. Queiroz, V.L. Ferracini, M.A. Rosa, Quimica Nova 35 (2012) 185. [12] X. Liu, X. Wang, J. Xu, F. Dong, W. Song, Y. Zheng, Biomed. Chromatogr. 25 (2011) 1081. [13] A. Bordagaray, R. Garcia-Arrona, E. Millan, Anal. Methods 5 (2013) 2565. [14] E.A.S. Silva, V. Lopez-Avila, J. Pawliszyn, J. Chromatogr. A 1313 (2013) 139. [15] M.G. Silva, A. Aquino, H.S. Doirea, S. Navickiene, Talanta 76 (2008) 680. [16] L. Lagunas-Allue, J. Sanz-Asensio, M.T. Martinez-Soria, Anal. Bioanal. Chem. 398 (2010) 1509. [17] S. Wang, Y. Xu, C. Pan, S. Jiang, F. Liu, Anal. Bioanal. Chem. 387 (2007) 673. [18] Association of Official Analytical Chemists, Official Methods of Analysis of the Association of Official Analytical Chemists, 1, 15th ed., Association of Official Analytical Chemists, 1990. [19] L. Lagunas-Allue, J. Sanz-Asensio, M.T. Martinez-Soria, J. Chromatogr. A 1270 (2012) 62. [20] P. Labarta, M.P. Martinez-Moral, M.T. Tena, Anal. Chem. 2012 (2012) 8. [21] E. Schreck, F. Geret, L. Gontier, M. Treilhou, Talanta 77 (2008) 298. [22] J.L.M. Vidal, J.A.P. Sanchez, P. Plaza-Bolaos, A.G. Frenich, R. Romero-Gonzalez, J. AOAC Int. 93 (2010) 1715. [23] T. Tanaka, T. Hori, T. Asada, K. Oikawa, K. Kawata, J. Chromatogr. A 1175 (2007) 181. [24] S.-K. Cho, A.M. Abd El-Aty, J.-H. Choi, Y.-M. Jeong, H.-C. Shin, B.-J. Chang, C. Lee, J.-H. Shim, J. Sep. Sci. 31 (2008) 1750. [25] S.J. Lehotay, T. Mary, G.A. Valverde, C. Mariano, M. Hans, H. Volkmar, A. Thomas, L. Gnter, F. Richard, M. Katerina, P. Mette Erecius, B. Amy, H. Walter, C. Jo Marie, A. Lutz, L. Karen, G. Miguel, H. Marvin, K. Andr de, H. Maurice, S. Frank, W. Anthony, P. Alesia, J. AOAC Int. 90 (2007) 485. [26] S. Walorczyk, D. Dro, J. AOAC Int. 94 (2011) 1625. [27] A.N. Brown, J.M. Cook, W.T. Hammack, J.S. Stepp, J.V. Pelt, G. Gerard, J. AOAC Int. 94 (2011) 931.
